This new worm isn't subtle.

I noticed a lot of people talking about how to vibecode without your project turning into slopware. Here is a beginner guide from someone whose done basically everything in software on how some minimal processes keeps your llms from getting away from you.

Deep Seek v4 Flash Is BEATING PRO AND GLM 5.2. Models that run on 4+ Sparks. Why use ALL 4 Sparks to Run GLM when you can run 2 Instances of DS4 or better yet run one instance and have 2 Sparks to go do other things with... This is Wild !

Deepseek V4 flash 0731 is "Pareto frontier" of intelligence and cost > almost impossible to find one that was both more powerful and cheaper.

Deepseek v4 flash beating GLM 5.2 is profound. I think we will be a catalyst for x2 DGX spark boxes. I believe we will see a GLM 5.2. The age of smart nimble models are upon us. Luna, @thinkymachines @Zai_org @deepseek_ai huggingface.co/unsloth/DeepS…

I'm starting the conversion work from new DeepSeek v4 Flash checkpoint to GGUF. If the model is as good as it looks, I'll probably remove the GGLM 5.2 support from the system, since now we have a model that is best suited for local inference that is smaller. Feedbacks?

Official DeepSeek v4 Flash weights are out in @huggingface 🔥🔥🔥 huggingface.co/deepseek-ai/D…

DeepSeek V4 Flash 0731 scores 50 on the Artificial Analysis Intelligence Index, a 10-point jump over DeepSeek V4 Flash (released April 2026) that puts it 6 points ahead of DeepSeek V4 Pro. It shares identical architecture and pricing with the earlier DeepSeek V4 Flash, and lands on our Pareto frontier for Intelligence vs Cost per Task @deepseek_ai’s DeepSeek V4 Flash 0731 is one Intelligence Index point behind GPT-5.6 Luna (max, 51). Even after OpenAI’s 80% price cut on GPT-5.6 Luna today, DeepSeek V4 Flash 0731’s Cost per Task on DeepSeek’s first-party API comes in at ~60% lower than GPT-5.6 Luna (max), a model with comparable intelligence. A key driver of this is DeepSeek’s ~98% cache hit discount on its first-party API, a significantly more aggressive discount than the 90% cache hit discount offered by most of the industry The new model is a significant step up from the previous generation, DeepSeek V4 Flash (40), and places the model within 1 point of GLM-5.2 (max, 51). It remains 7 points behind the open weights frontier set by Kimi K3 (max, 57). For additional context, this places the model in line with recently released Gemini 3.6 Flash (50) and 1 point behind Muse Spark 1.1 (xhigh, 51). DeepSeek is expected to release the model’s full weights in the coming weeks DeepSeek V4 Flash 0731 retains a 1M token context window, and its size remains unchanged from DeepSeek V4 Flash at 284B total parameters and 13B active at inference time Key results: ➤ Improvements in agentic performance: DeepSeek V4 Flash 0731 achieves an Elo rating of 1559 on GDPval-AA v2, our evaluation focused on agentic real-world work tasks, up from 1189 for the previous DeepSeek V4 Flash. Once weights are released this will be the second highest open weights score, behind Kimi K3 (max, 1687) and ahead of GLM-5.2 (max, 1510). Terminal-Bench 2.1 rises 17 points to 79% and τ³-Bench Banking 8 points to 31% ➤ Token usage falls 12% against the predecessor: DeepSeek V4 Flash 0731 used ~206M output tokens to run the Intelligence Index, against ~234M for the previous DeepSeek V4 Flash. The new variant is more token efficient, achieving a higher Intelligence Index with a lower number of total output tokens ➤ DeepSeek V4 Flash 0731 improves over its predecessor on every evaluation in the Intelligence Index: Alongside the agentic gains, CritPt gains 9 points to 17%, SciCode 5 points to 50%, Humanity's Last Exam 5 points to 37%, AA-LCR 3 points to 66% and GPQA Diamond 1 point to 91% ➤ AA-Omniscience improvements are driven by fewer hallucinations, rather than higher accuracy: DeepSeek V4 Flash 0731 achieves an AA-Omniscience Index of -16, a +7 improvement from its predecessor. This improvement is purely driven by a reduced hallucination rate, with overall accuracy (percentage correct) unchanged. Its AA-Omniscience Hallucination Rate is 84%, a 12 point decrease from its predecessor, and comparable to models such as GPT-5.6 Terra (max, 85%) and Mistral Medium 3.5 (82%) Additional model details: ➤ Context window: 1M tokens (equivalent to DeepSeek V4 Flash) ➤ Size: 284B total parameters (13B active) ➤ Input modalities: Text input and output only ➤ Accessibility: Available through DeepSeek’s first-party API ➤ Pricing: $0.14/$0.28 per 1M input/output tokens, unchanged from DeepSeek V4 Flash. Cache hit price of $0.0028 per 1M tokens, a 98% discount

hell yeah. we’re now 90% faster for laguna XS!!! .@ivanfioravanti cooking hard!!! 🔥

Now with the upcoming release of the new DS4F checkpoint, and I believe later with Laguna S2.2 or a new S2.1 checkpoint, DwarfStar is going to be better at zero development costs. It is important to invest in the right models that get better and are locally runnable.